# Robust Nonlinear Transform Coding: A Framework for Generalizable Joint Source-Channel Coding

arXiv: https://arxiv.org/abs/2511.18884 · HTML: https://mlsanity.com/paper/2511.18884/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 24 Nov 2025.

## Builds on (most cited first, 16 of 16)

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- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
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- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [End-to-end Optimized Image Compression](https://mlsanity.com/paper/1611.01704/) (2016)
- [Nonlinear Transform Coding](https://mlsanity.com/paper/2007.03034/) (2020)
- [Nonlinear Transform Source-Channel Coding for Semantic Communications](https://mlsanity.com/paper/2112.10961/) (2021)
- [Deep Joint Source-Channel Coding for Adaptive Image Transmission over MIMO Channels](https://mlsanity.com/paper/2309.00470/) (2023)
- [OFDM-Based Digital Semantic Communication with Importance Awareness](https://mlsanity.com/paper/2401.02178/) (2024)
- [From Analog to Digital: Multi-Order Digital Joint Coding-Modulation for Semantic Communication](https://mlsanity.com/paper/2406.05437/) (2024)
- [New Radio Physical Layer Abstraction for System-Level Simulations of 5G Networks](https://mlsanity.com/paper/2001.10309/) (2020)
- [Universal Joint Source-Channel Coding for Modulation-Agnostic Semantic Communication](https://mlsanity.com/paper/2405.10749/) (2024)
- [Blind Training for Channel-Adaptive Digital Semantic Communications](https://mlsanity.com/paper/2501.02273/) (2025)
- [Multi-user Wireless Image Semantic Transmission over MIMO Multiple Access Channels](https://mlsanity.com/paper/2504.07969/) (2025)
- [ESC-MVQ: End-to-End Semantic Communication With Multi-Codebook Vector Quantization](https://mlsanity.com/paper/2504.11709/) (2025)

## Abstract (arXiv)

This paper proposes robust nonlinear transform coding (Robust-NTC), a generalizable digital joint source-channel coding (JSCC) framework that couples variational latent modeling with channel-adaptive transmission. Unlike learning-based JSCC methods that implicitly absorb channel variations, Robust-NTC explicitly models element-wise latent distributions via a variational objective with a Gaussian proxy for quantization and channel noise, allowing encoder-decoder to capture latent uncertainty without channel-specific training. Using the learned statistics, Robust-NTC also facilitates rate-distortion optimization to adaptively select element-wise quantizers and bit depths according to online channel conditions. To support practical deployment, Robust-NTC is integrated into an orthogonal frequency-division multiplexing (OFDM) system, where a unified resource allocation framework jointly optimizes latent quantization, bit allocation, modulation order, and power allocation to minimize transmission latency while guaranteeing learned distortion targets. Simulation results demonstrate that for practical OFDM systems, Robust-NTC achieves superior rate-distortion efficiency and stable reconstruction fidelity compared to both a conventional separated coding scheme and digital JSCC baselines across various channel conditions.